{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T20:44:45Z","timestamp":1788813885265,"version":"build-2803163510"},"reference-count":116,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,4,15]],"date-time":"2022-04-15T00:00:00Z","timestamp":1649980800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Mantle cell lymphoma (MCL) is a rare lymphoid malignancy with a poor prognosis characterised by frequent relapse and short durations of treatment response. Most patients present with aggressive disease, but there exist indolent subtypes without the need for immediate intervention. The very heterogeneous behaviour of MCL is genetically characterised by the translocation t(11;14)(q13;q32), leading to Cyclin D1 overexpression with distinct clinical and biological characteristics and outcomes. There is still an unfulfilled need for precise MCL prognostication in real-time. Machine learning and deep learning neural networks are rapidly advancing technologies with promising results in numerous fields of application. This study develops and compares the performance of deep learning (DL) algorithms and radiomics-based machine learning (ML) models to predict MCL relapse on baseline CT scans. Five classification algorithms were used, including three deep learning models (3D SEResNet50, 3D DenseNet, and an optimised 3D CNN) and two machine learning models based on K-nearest Neighbor (KNN) and Random Forest (RF). The best performing method, our optimised 3D CNN, predicted MCL relapse with a 70% accuracy, better than the 3D SEResNet50 (62%) and the 3D DenseNet (59%). The second-best performing method was the KNN-based machine learning model (64%) after principal component analysis for improved accuracy. Our optimised CNN developed by ourselves correctly predicted MCL relapse in 70% of the patients on baseline CT imaging. Once prospectively tested in clinical trials with a larger sample size, our proposed 3D deep learning model could facilitate clinical management by precision imaging in MCL.<\/jats:p>","DOI":"10.3390\/cancers14082008","type":"journal-article","created":{"date-parts":[[2022,4,16]],"date-time":"2022-04-16T07:42:41Z","timestamp":1650094961000},"page":"2008","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Deep Neural Networks and Machine Learning Radiomics Modelling for Prediction of Relapse in Mantle Cell Lymphoma"],"prefix":"10.3390","volume":"14","author":[{"given":"Catharina Silvia","family":"Lisson","sequence":"first","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany"},{"name":"Center for Personalized Medicine (ZPM), University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany"},{"name":"Artificial Intelligence in Experimental Radiology (XAIRAD)"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christoph Gerhard","family":"Lisson","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marc Fabian","family":"Mezger","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany"},{"name":"Artificial Intelligence in Experimental Radiology (XAIRAD)"},{"name":"Visual Computing Group, Institute of Media Informatics, Ulm University, James-Franck-Ring, 89081 Ulm, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8584-5189","authenticated-orcid":false,"given":"Daniel","family":"Wolf","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany"},{"name":"Artificial Intelligence in Experimental Radiology (XAIRAD)"},{"name":"Visual Computing Group, Institute of Media Informatics, Ulm University, James-Franck-Ring, 89081 Ulm, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7954-4542","authenticated-orcid":false,"given":"Stefan Andreas","family":"Schmidt","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany"},{"name":"Center for Personalized Medicine (ZPM), University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wolfgang M.","family":"Thaiss","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany"},{"name":"Artificial Intelligence in Experimental Radiology (XAIRAD)"},{"name":"Department of Nuclear Medicine, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eugen","family":"Tausch","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine III, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany"},{"name":"Comprehensive Cancer Center Ulm (CCCU), University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9042-7806","authenticated-orcid":false,"given":"Ambros J.","family":"Beer","sequence":"additional","affiliation":[{"name":"Center for Personalized Medicine (ZPM), University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany"},{"name":"Artificial Intelligence in Experimental Radiology (XAIRAD)"},{"name":"Department of Nuclear Medicine, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany"},{"name":"Center for Translational Imaging \u201cFrom Molecule to Man\u201d (MoMan), Department of Internal Medicine II, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany"},{"name":"i2SouI\u2014Innovative Imaging in Surgical Oncology Ulm, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephan","family":"Stilgenbauer","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine III, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany"},{"name":"Comprehensive Cancer Center Ulm (CCCU), University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7523-1979","authenticated-orcid":false,"given":"Meinrad","family":"Beer","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany"},{"name":"Center for Personalized Medicine (ZPM), University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany"},{"name":"Artificial Intelligence in Experimental Radiology (XAIRAD)"},{"name":"Center for Translational Imaging \u201cFrom Molecule to Man\u201d (MoMan), Department of Internal Medicine II, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany"},{"name":"i2SouI\u2014Innovative Imaging in Surgical Oncology Ulm, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0984-224X","authenticated-orcid":false,"given":"Michael","family":"Goetz","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany"},{"name":"Artificial Intelligence in Experimental Radiology (XAIRAD)"},{"name":"German Cancer Research Center (DKFZ), Division Medical Image Computing, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1111\/bjh.14699","article-title":"Incidence and survival trends in mantle cell lymphoma","volume":"181","author":"Epperla","year":"2017","journal-title":"Br. J. Haematol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2375","DOI":"10.1182\/blood-2016-01-643569","article-title":"The 2016 revision of the World Health Organization classification of lymphoid neoplasms","volume":"127","author":"Swerdlow","year":"2016","journal-title":"Blood"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2770","DOI":"10.1200\/JCO.2006.08.7999","article-title":"Quantitative gene expression deregulation in mantle-cell lymphoma: Correlation with clinical and biologic factors","volume":"25","author":"Kienle","year":"2007","journal-title":"J. Clin. Oncol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/S1535-6108(03)00028-X","article-title":"The proliferation gene expression signature is a quantitative integrator of oncogenic events that predicts survival in mantle cell lymphoma","volume":"3","author":"Rosenwald","year":"2003","journal-title":"Cancer Cell"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1216","DOI":"10.1200\/JCO.2006.08.4251","article-title":"Specific secondary genetic alterations in mantle cell lymphoma provide prognostic information independent of the gene expression\u2013based proliferation signature","volume":"25","author":"Salaverria","year":"2007","journal-title":"J. Clin. Oncol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1111\/j.1365-2141.2005.05716.x","article-title":"Histopathology, cell proliferation indices and clinical outcome in 304 patients with mantle cell lymphoma (MCL): A clinicopathological study from the European MCL Network","volume":"131","author":"Tiemann","year":"2005","journal-title":"Br. J. Haematol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"iv62","DOI":"10.1093\/annonc\/mdx223","article-title":"Newly diagnosed and relapsed mantle cell lymphoma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up","volume":"28","author":"Dreyling","year":"2017","journal-title":"Ann. Oncol."},{"key":"ref_8","unstructured":"Swerdlow, S.H., Campo, E., Harris, N.L., Jaffe, E.S., Pileri, S.A., Stein, H., Thiele, J., and Vardiman, J.W. (2017). WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues. WHO Classification of Tumours, WHO Press. [4th ed.]."},{"key":"ref_9","first-page":"2687","article-title":"Long-term progression-free survival of mantle cell lymphoma after intensive front-line immu-nochemotherapy with in vivo\u2013purged stem cell rescue: A nonrandomized phase 2 multicenter study by the Nordic Lymphoma Group","volume":"112","author":"Geisler","year":"2008","journal-title":"Blood. J. Am. Soc. Hematol."},{"key":"ref_10","first-page":"2715","article-title":"What can artificial intelligence teach us about the molecular mechanisms underlying disease?","volume":"46","author":"Cook","year":"2019","journal-title":"Eur. J. Pediatr."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.ejca.2011.11.036","article-title":"Radiomics: Extracting more information from medical images using advanced feature analysis","volume":"48","author":"Lambin","year":"2012","journal-title":"Eur. J. Cancer"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1038\/nrclinonc.2017.141","article-title":"Radiomics: The bridge between medical imaging and personalized medicine","volume":"14","author":"Lambin","year":"2017","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1136","DOI":"10.12688\/f1000research.14122.1","article-title":"Current overview and treatment of mantle cell lymphoma","volume":"7","author":"Schieber","year":"2018","journal-title":"F1000Research"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2927","DOI":"10.1182\/bloodadvances.2019001350","article-title":"Genetic mutations and features of mantle cell lymphoma: A systematic review and meta-analysis","volume":"4","author":"Hill","year":"2020","journal-title":"Blood Adv."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.1182\/blood.2020005289","article-title":"Genomic and epigenomic insights into the origin, pathogenesis, and clinical behavior of mantle cell lymphoma subtypes","volume":"136","author":"Nadeu","year":"2020","journal-title":"Blood"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1182\/blood-2007-06-095331","article-title":"A new prognostic index (MIPI) for patients with advanced-stage mantle cell lymphoma","volume":"111","author":"Hoster","year":"2008","journal-title":"Blood"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1386","DOI":"10.1200\/JCO.2015.63.8387","article-title":"Prognostic value of Ki-67 index, cytology, and growth pattern in mantle-cell lymphoma: Results from randomized trials of the European Mantle Cell Lymphoma Network","volume":"34","author":"Hoster","year":"2016","journal-title":"J. Clin. Oncol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1270","DOI":"10.1016\/j.bbmt.2006.07.007","article-title":"Rituximab induces effective clearance of minimal residual disease in molecular relapses of mantle cell lymphoma","volume":"12","author":"Ladetto","year":"2006","journal-title":"Biol. Blood Marrow Transplant."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3215","DOI":"10.1182\/blood-2009-06-230250","article-title":"Molecular remission is an independent predictor of clinical outcome in patients with mantle cell lymphoma after combined immunochemotherapy: A European MCL intergroup study","volume":"115","author":"Pott","year":"2010","journal-title":"Blood"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1209","DOI":"10.1200\/JCO.2008.19.6121","article-title":"Outcome of deferred initial therapy in mantle-cell lymphoma","volume":"27","author":"Martin","year":"2009","journal-title":"J. Clin. Oncol."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Steinbuss, G., Kriegsmann, M., Zgorzelski, C., Brobeil, A., Goeppert, B., Dietrich, S., Mechtersheimer, G., and Kriegsmann, K. (2021). Deep Learning for the Classification of Non-Hodgkin Lymphoma on Histopathological Images. Cancers, 13.","DOI":"10.3390\/cancers13102419"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1007\/s13244-012-0196-6","article-title":"Assessment of tumor heterogeneity: An emerging imaging tool for clinical practice?","volume":"3","author":"Davnall","year":"2012","journal-title":"Insights Imaging"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1038\/nature12625","article-title":"The causes and consequences of genetic heterogeneity in cancer evolution","volume":"501","author":"Burrell","year":"2013","journal-title":"Nature"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"85","DOI":"10.3389\/fmed.2018.00085","article-title":"Overview on clinical relevance of intra-tumor heterogeneity","volume":"5","author":"Stanta","year":"2018","journal-title":"Front. Med."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1159\/000475530","article-title":"Tumor heterogeneity in lymphomas: A different breed","volume":"85","author":"Federmann","year":"2018","journal-title":"Pathobiology"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1148\/radiol.2015151169","article-title":"Radiomics: Images are more than pictures, they are data","volume":"278","author":"Gillies","year":"2016","journal-title":"Radiology"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.ymeth.2020.05.022","article-title":"Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework","volume":"188","author":"Ibrahim","year":"2021","journal-title":"Methods"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2656","DOI":"10.1007\/s00259-019-04372-x","article-title":"Towards clinical application of image mining: A systematic review on artificial intelligence and radiomics","volume":"46","author":"Sollini","year":"2019","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2760","DOI":"10.1007\/s00259-019-04420-6","article-title":"Radiomic features of glucose metabolism enable prediction of outcome in mantle cell lymphoma","volume":"46","author":"Mayerhoefer","year":"2019","journal-title":"Eur. J. Nucl. Med. Mol. Pediatr."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4329","DOI":"10.1038\/s41598-019-40437-5","article-title":"The complexity of tumor shape, spiculatedness, correlates with tumor radiomic shape features","volume":"9","author":"Limkin","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"21","DOI":"10.3350\/cmh.2018.1007","article-title":"Radiomics and radiogenomics of primary liver cancers","volume":"25","author":"Jeong","year":"2019","journal-title":"Clin. Mol. Hepatol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3764","DOI":"10.1007\/s00261-019-02042-y","article-title":"Novel imaging techniques of rectal cancer: What do radiomics and radiogenomics have to offer? A literature review","volume":"44","author":"Horvat","year":"2019","journal-title":"Abdom. Radiol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1016\/j.nicl.2018.08.024","article-title":"Combined FET PET\/MRI radiomics differentiates radiation injury from recurrent brain metastasis","volume":"20","author":"Lohmann","year":"2018","journal-title":"NeuroImage Clin."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1038\/s41523-018-0078-2","article-title":"Exploration of PET and MRI radiomic features for decoding breast cancer phenotypes and prognosis","volume":"4","author":"Huang","year":"2018","journal-title":"Npj Breast Cancer"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1631\/jzus.B1700260","article-title":"Towards precision medicine: From quantitative imaging to radiomics","volume":"19","author":"Acharya","year":"2018","journal-title":"J. Zhejiang Univ. Sci. B"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1016\/j.radonc.2016.04.004","article-title":"Radiomic phenotype features predict pathological response in non-small cell lung cancer","volume":"119","author":"Coroller","year":"2016","journal-title":"Radiother. Oncol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.tranon.2016.01.008","article-title":"Radiomics Analysis on FLT-PET\/MRI for Characterization of Early Treatment Response in Renal Cell Carcinoma: A Proof-of-Concept Study","volume":"9","author":"Antunes","year":"2016","journal-title":"Transl. Oncol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1148\/radiol.2020191145","article-title":"The image biomarker standardization initiative: Standardized quantitative radiomics for high-throughput image-based phenotyping","volume":"295","author":"Zwanenburg","year":"2020","journal-title":"Radiology"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1038\/s41598-020-57739-8","article-title":"Optimal statistical incorporation of independent feature stability information into radiomics studies","volume":"10","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_40","first-page":"2638","article-title":"Radiomics in nuclear medicine: Robustness, reproducibility, standardization, and how to avoid data analysis traps and replication crisis","volume":"46","author":"Zwanenburg","year":"2019","journal-title":"Eur. J. Pediatr."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"105153","DOI":"10.1016\/j.cmpb.2019.105153","article-title":"Texture analysis and multiple-instance learning for the classification of malignant lymphomas","volume":"185","author":"Lippi","year":"2019","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"844","DOI":"10.3389\/fonc.2019.00844","article-title":"Three-dimensional texture analysis based on PET\/CT images to distinguish hepatocellular carcinoma and hepatic lymphoma","volume":"9","author":"Xu","year":"2019","journal-title":"Front. Oncol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1292","DOI":"10.1007\/s10147-019-01482-2","article-title":"Volumetric and texture analysis on FDG PET in evaluating and predicting treatment response and recurrence after chemotherapy in follicular lymphoma","volume":"24","author":"Tatsumi","year":"2019","journal-title":"Int. J. Clin. Oncol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1012","DOI":"10.1007\/s00330-016-4470-8","article-title":"CT-based texture analysis potentially provides prognostic information complementary to interim fdg-pet for patients with hodgkin\u2019s and aggressive non-hodgkin\u2019s lymphomas","volume":"27","author":"Ganeshan","year":"2017","journal-title":"Eur. Radiol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"101188","DOI":"10.1016\/j.tranon.2021.101188","article-title":"CT-based radiomics model with machine learning for predicting primary treatment failure in diffuse large B-cell Lymphoma","volume":"14","author":"Santiago","year":"2021","journal-title":"Transl. Oncol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"6228","DOI":"10.1007\/s00330-020-06927-1","article-title":"Current status and quality of radiomics studies in lymphoma: A systematic review","volume":"30","author":"Wang","year":"2020","journal-title":"Eur. Radiol."},{"key":"ref_47","first-page":"72","article-title":"Differential diagnostic ability of 18F-FDG PET\/CT radiomics features between renal cell carcinoma and renal lymphoma","volume":"65","author":"Zhu","year":"2019","journal-title":"Q. J. Nucl. Med. Mol. Imaging"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1322","DOI":"10.1038\/s41598-018-37197-z","article-title":"A PET radiomics model to predict refractory mediastinal hodgkin lymphoma","volume":"9","author":"Milgrom","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3832","DOI":"10.1007\/s00330-018-5368-4","article-title":"Primary central nervous system lymphoma and atypical glioblastoma: Differentiation using radiomics approach","volume":"28","author":"Suh","year":"2018","journal-title":"Eur. Radiol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1251","DOI":"10.1093\/neuonc\/noy021","article-title":"Diffusion radiomics as a di-agnostic model for atypical manifestation of primary central nervous system lymphoma: Development and multicenter external validation","volume":"20","author":"Kang","year":"2018","journal-title":"Neuro-Oncology"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"109214","DOI":"10.1016\/j.ejrad.2020.109214","article-title":"Mid-term response assessment in multiple myeloma using a texture analysis approach on dual energy-CT-derived bone marrow images\u2014A proof of principle study","volume":"131","author":"Reinert","year":"2020","journal-title":"Eur. J. Radiol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"6911","DOI":"10.1007\/s00330-019-06291-9","article-title":"Computed tomography textural analysis for the differentiation of chronic lymphocytic leukemia and diffuse large B cell lymphoma of Richter syndrome","volume":"29","author":"Reinert","year":"2019","journal-title":"Eur. Radiol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.ejrad.2018.05.010","article-title":"Discriminatory CT-textural features in splenic infiltration of lymphoma versus splenomegaly in liver cirrhosis versus normal spleens in controls and evaluation of their role for longitudinal lymphoma monitoring","volume":"104","author":"Reinert","year":"2018","journal-title":"Eur. J. Radiol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.neurad.2014.02.006","article-title":"TCGA Glioma Phenotype Research Group Addition of MR imaging features and genetic biomarkers strengthens glioblastoma survival prediction in TCGA patients","volume":"42","author":"Nicolasjilwan","year":"2015","journal-title":"J. Neuroradiol."},{"key":"ref_55","unstructured":"Mohri, M., Rostamizadeh, A., and Talwalkar, A. (2012). Foundations of Machine Learning, MIT Press. Chapter 1."},{"key":"ref_56","unstructured":"Gero, J.S., and Sudweeks, F. (1996). Automated Design of Both the Topology and Sizing of Analog Electrical Circuits Using Genetic Programming. Artificial Intelligence in Design\u2019 96, Springer."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"4462","DOI":"10.1021\/acs.molpharmaceut.7b00578","article-title":"Comparison of deep learning with multiple machine learning methods and metrics using diverse drug discovery data sets","volume":"14","author":"Korotcov","year":"2017","journal-title":"Mol. Pharm."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1109\/TMI.2016.2553401","article-title":"Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique","volume":"35","author":"Greenspan","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural networks: An overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1007\/s10278-016-9914-9","article-title":"High-throughput classification of radiographs using deep convolutional neural networks","volume":"30","author":"Rajkomar","year":"2017","journal-title":"J. Digit. Imaging"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural Features for Image Classification","volume":"SMC-3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Duin, R.P., and Pekalska, E. (2005). Dissimilarity Representation for Pattern Recognition, The: Foundations and Applications, World Scientific.","DOI":"10.1142\/9789812703170"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"S\u00e1nchez-Maro\u00f1o, N., Alonso-Betanzos, A., and Tombilla-Sanrom\u00e1n, M. (2007). Filter Methods for Feature Selection\u2014A Comparative Study. International Conference on Intelligent Data Engineering and Automated Learning, Springer.","DOI":"10.1007\/978-3-540-77226-2_19"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Alhaj, T.A., Siraj, M.M., Zainal, A., Elshoush, H.T., and Elhaj, F. (2016). Feature selection using information gain for improved structural-based alert correlation. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0166017"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Kira, K., and Rendell, L.A. (1992). A Practical Approach to Feature Selection. Machine Learning Proceedings 1992, Elsevier.","DOI":"10.1016\/B978-1-55860-247-2.50037-1"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.jbi.2018.07.014","article-title":"Relief-based feature selection: Introduction and review","volume":"85","author":"Urbanowicz","year":"2018","journal-title":"J. Biomed. Inform."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1145\/1656274.1656278","article-title":"The WEKA data mining software: An update","volume":"11","author":"Hall","year":"2009","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Kononenko, I. (1994, January 6\u20138). Estimating attributes: Analysis and extensions of RELIEF. Proceedings of the European Conference on Machine Learning, Catania, Italy.","DOI":"10.1007\/3-540-57868-4_57"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"5063","DOI":"10.1007\/s10489-021-02659-x","article-title":"A novel filter feature selection algorithm based on relief","volume":"52","author":"Cui","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1023\/A:1008280620621","article-title":"Overcoming the myopia of inductive learning algorithms with RELIEFF","volume":"7","author":"Kononenko","year":"1997","journal-title":"Appl. Intell."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Sidey-Gibbons, J.A.M., and Sidey-Gibbons, C.J. (2019). Machine learning in medicine: A practical introduction. BMC Med. Res. Methodol., 19.","DOI":"10.1186\/s12874-019-0681-4"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1347","DOI":"10.1056\/NEJMra1814259","article-title":"Machine learning in medicine","volume":"380","author":"Rajkomar","year":"2019","journal-title":"N. Engl. J. Med."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"13087","DOI":"10.1038\/srep13087","article-title":"Machine Learning methods for Quantitative Radiomic Biomarkers","volume":"5","author":"Parmar","year":"2015","journal-title":"Sci. Rep."},{"key":"ref_74","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_76","first-page":"18","article-title":"Classification and regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Laaksonen, J., and Oja, E. (1996, January 3\u20136). Classification with learning k-nearest neighbors. Proceedings of the International Conference on Neural Networks (ICNN\u203296), Washington, DC, USA.","DOI":"10.1109\/ICNN.1996.549118"},{"key":"ref_78","unstructured":"Kayalibay, B., Jensen, G., and van der Smagt, P. (2017). CNN-based segmentation of medical imaging data. arXiv."},{"key":"ref_79","first-page":"327","article-title":"Deep learning convolutional neural network (DLCNN): Unleashing the potential of (18)F-FDG PET\/CT in lymphoma","volume":"11","author":"Li","year":"2021","journal-title":"Am. J. Nucl. Med. Mol. Imaging"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1170","DOI":"10.1109\/TMI.2015.2482920","article-title":"Improving computer-aided detection using convolutional neural networks and random view aggregation","volume":"35","author":"Roth","year":"2015","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1148\/radiol.2019191114","article-title":"18F-FDG PET\/CT uptake classification in lymphoma and lung cancer by using deep convolutional neural networks","volume":"294","author":"Sibille","year":"2020","journal-title":"Radiology"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"3665","DOI":"10.1002\/mp.14847","article-title":"Diffuse large B-cell lymphoma segmentation in PET-CT images via hybrid learning for feature fusion","volume":"48","author":"Yuan","year":"2021","journal-title":"Med. Phys."},{"key":"ref_83","first-page":"1362","article-title":"Fully automatic segmentation of diffuse large B cell lymphoma lesions on 3D FDG-PET\/CT for total metabolic tumour volume prediction using a convolutional neural network","volume":"48","author":"Kanoun","year":"2020","journal-title":"Eur. J. Nucl. Med. Mol. Pediatr."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"12611","DOI":"10.1038\/s41598-018-30657-6","article-title":"Deep Learning and Radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer","volume":"8","author":"Bibault","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"710","DOI":"10.1002\/ajh.25487","article-title":"Mantle cell lymphoma: 2019 update on the diagnosis, pathogenesis, prognostication, and management","volume":"94","author":"Jain","year":"2019","journal-title":"Am. J. Hematol."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"2489","DOI":"10.1093\/annonc\/mdx333","article-title":"Observation as the initial management strategy in patients with mantle cell lymphoma","volume":"28","author":"Abrisqueta","year":"2017","journal-title":"Ann. Oncol."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"20190948","DOI":"10.1259\/bjr.20190948","article-title":"Radiomics: From qualitative to quantitative imaging","volume":"93","author":"Rogers","year":"2020","journal-title":"Br. J. Radiol."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"4006","DOI":"10.1038\/ncomms5006","article-title":"Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach","volume":"5","author":"Aerts","year":"2014","journal-title":"Nat. Commun."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.cllc.2016.02.001","article-title":"Radiomic features are associated with EGFR mutation status in lung adenocarcinomas","volume":"17","author":"Liu","year":"2016","journal-title":"Clin. Lung Cancer"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"e1753","DOI":"10.1097\/MD.0000000000001753","article-title":"Decoding tumor phenotypes for ALK, ROS1, and RET fusions in lung adenocarcinoma using a radiomics approach","volume":"94","author":"Yoon","year":"2015","journal-title":"Medicine"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1038\/nrclinonc.2017.166","article-title":"Tumour heterogeneity and resistance to cancer therapies","volume":"15","author":"Shaw","year":"2018","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"622","DOI":"10.1007\/s10278-017-0009-z","article-title":"Residual Deep Convolutional Neural Network Predicts MGMT Methylation Status","volume":"30","author":"Korfiatis","year":"2017","journal-title":"J. Digit. Imaging"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"601","DOI":"10.3390\/make3030031","article-title":"Orientation-Encoding CNN for Point Cloud Classification and Segmentation","volume":"3","author":"Lin","year":"2021","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"695","DOI":"10.3390\/make3030035","article-title":"Benchmarking Studies Aimed at Clustering and Classification Tasks Using K-Means, Fuzzy C-Means and Evolutionary Neural Networks","volume":"3","author":"Pickens","year":"2021","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"466","DOI":"10.3390\/make1010028","article-title":"Using resistin, glucose, age and BMI and pruning fuzzy neural network for the construction of expert systems in the prediction of breast cancer","volume":"1","author":"Rezende","year":"2019","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"575","DOI":"10.3390\/make1020034","article-title":"Towards robust text classification with semantics-aware recurrent neural archi-tecture","volume":"1","author":"Kralj","year":"2019","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"189","DOI":"10.2967\/jnumed.117.200501","article-title":"Responsible Radiomics Research for Faster Clinical Translation","volume":"59","author":"Zwanenburg","year":"2018","journal-title":"J. Nucl. Med."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1148\/radiol.2018172361","article-title":"Radiomics of CT Features May Be Nonreproducible and Redundant: Influence of CT Acquisition Parameters","volume":"288","author":"Berenguer","year":"2018","journal-title":"Radiology"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"960","DOI":"10.1016\/j.molonc.2014.10.004","article-title":"Biomarker validation and testing","volume":"9","author":"Hayes","year":"2014","journal-title":"Mol. Oncol."},{"key":"ref_100","first-page":"e1","article-title":"Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE-MRI derived biomarkers in multicenter oncology trials","volume":"49","author":"Obuchowski","year":"2019","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"13047","DOI":"10.1038\/s41598-018-31509-z","article-title":"Comprehensive investigation on controlling for CT imaging variabilities in radiomics studies","volume":"8","author":"Ger","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1097\/RLI.0000000000000666","article-title":"Variability and standardization of quantitative imaging: Monoparametric to multiparametric quantification, radiomics, and artificial intelligence","volume":"55","author":"Hagiwara","year":"2020","journal-title":"Investig. Radiol."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13244-019-0832-5","article-title":"Deep learning workflow in radiology: A primer","volume":"11","author":"Montagnon","year":"2020","journal-title":"Insights Imaging"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"485","DOI":"10.5152\/dir.2019.19321","article-title":"Radiomics with artificial intelligence: A practical guide for beginners","volume":"25","author":"Kocak","year":"2019","journal-title":"Diagn. Interv. Radiol."},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Rogasch, J.M.M., Hundsdoerfer, P., Hofheinz, F., Wedel, F., Schatka, I., Amthauer, H., and Furth, C. (2018). Pretherapeutic FDG-PET total metabolic tumor volume predicts response to induction therapy in pediatric Hodgkin\u2019s lymphoma. BMC Cancer, 18.","DOI":"10.1186\/s12885-018-4432-4"},{"key":"ref_106","first-page":"1","article-title":"Radiomics in medical imaging\u2014\u201cHow-to\u201d guide and critical reflection","volume":"11","author":"Cester","year":"2020","journal-title":"Insights Imaging"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"e200016","DOI":"10.1148\/ryai.2020200016","article-title":"Convolutional neural networks for automated PET\/CT detection of diseased lymph node burden in patients with lymphoma","volume":"2","author":"Weisman","year":"2020","journal-title":"Radiol. Artif. Intell."},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Mayerhoefer, M.E., Riedl, C.C., Kumar, A., Dogan, A., Gibbs, P., Weber, M., Staber, P.B., Huicochea Castellanos, S., and Sch\u00f6der, H. (2020). [18F] FDG-PET\/CT radiomics for prediction of bone marrow involvement in mantle cell lymphoma: A retrospective study in 97 patients. Cancers, 12.","DOI":"10.3390\/cancers12051138"},{"key":"ref_109","first-page":"260","article-title":"Computer-aided detection of mantle cell lymphoma on 18F-FDG PET\/CT using a deep learning convolutional neural network","volume":"11","author":"Zhou","year":"2021","journal-title":"Am. J. Nucl. Med. Mol. Imaging"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1007\/s12149-019-01354-9","article-title":"Prognostic role of baseline 18F-FDG PET\/CT metabolic parameters in mantle cell lymphoma","volume":"33","author":"Albano","year":"2019","journal-title":"Ann. Nucl. Med."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1002\/ajh.22126","article-title":"Utility of positron emission tomography scans in mantle cell lymphoma","volume":"86","author":"Hosein","year":"2011","journal-title":"Am. J. Hematol."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"e33","DOI":"10.3324\/haematol.2019.223016","article-title":"Prognostic value of FDG-PET in patients with mantle cell lymphoma: Results from the LyMa-PET Project","volume":"105","author":"Bailly","year":"2020","journal-title":"Haematologica"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.1007\/s00259-010-1469-2","article-title":"Prognostic impact of 18F-fluoro-deoxyglucose positron emission tomography in untreated mantle cell lymphoma: A retrospective study from the GOELAMS group","volume":"37","author":"Touzeau","year":"2010","journal-title":"Eur. J. Nucl. Med. Mol. Pediatr."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1097\/MNM.0b013e32832e0c13","article-title":"FDG positron emission tomography\/computed tomography scan may identify mantle cell lymphoma patients with unusually favorable outcome","volume":"30","author":"Karam","year":"2009","journal-title":"Nucl. Med. Commun."},{"key":"ref_115","first-page":"3133","article-title":"Do we need hundreds of classifiers to solve real world classification problems?","volume":"15","author":"Cernadas","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1093\/jnci\/dji128","article-title":"Cancer risk pre-diction models: A workshop on development, evaluation, and application","volume":"97","author":"Freedman","year":"2005","journal-title":"J. Natl. Cancer Inst."}],"container-title":["Cancers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-6694\/14\/8\/2008\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:54:56Z","timestamp":1760136896000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-6694\/14\/8\/2008"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,15]]},"references-count":116,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["cancers14082008"],"URL":"https:\/\/doi.org\/10.3390\/cancers14082008","relation":{},"ISSN":["2072-6694"],"issn-type":[{"value":"2072-6694","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,15]]}}}